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                <h1 id="奶茶鼠的想法"><a href="#奶茶鼠的想法" class="headerlink" title="奶茶鼠的想法"></a>奶茶鼠的想法</h1><p>最近的心情：开心😁</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/190e61d4b22d14d4fc76eb57703da5f47e3aea74.jpg@1036w.webp" alt="img" style="zoom: 33%;"></p>
<h1 id="yolo-v1"><a href="#yolo-v1" class="headerlink" title="yolo v1"></a>yolo v1</h1><p>YOLO算法是一个经典的one stage算法，它的英文全称是You Only Look Once，意思就是在神经网络过一遍就能直接出结果。</p>
<p>首次提出是在2016CVPR中《You Look Only Once:Unified,Real-Time Object Detection》。在448*448图像上能达到45FPS和63.4map。</p>
<h2 id="算法思想"><a href="#算法思想" class="headerlink" title="算法思想"></a>算法思想</h2><p>1) 将一幅图像分成SxS个网格(grid cell)，如果某个object的中心 落在这个网格中，则这个网格就负责预测这个object。<br>2) 每个网格要预测B个bounding box，每个bounding box 除了要预测位置之外，还要附带预测一个confidence（置信度）值。每个网格还要预测C个类别的分数。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413152926436.png" alt="image-20230413152926436"></p>
<h2 id="网络结构"><a href="#网络结构" class="headerlink" title="网络结构"></a>网络结构</h2><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/e1704eb47c8d4cfd8fd8fe92024da599.png" alt="yolo1网络"></p>
<p>我们首先要给CNN网络输入一个448x448x3的图，这里要注意，在YOLO_v1版本中，由于网络中还保留有全连接层，因此输入的图片必须得是448x448x3。之后，图片会在CNN网络中经过加工，最终得到7x7x30的图。</p>
<p>CNN网络在这里的作用就是，使图的特征浓缩，即<strong>削减图的广度，提升图的深度</strong>。用深度学习的专业术语，就是图的<strong>感受野</strong>变大了。</p>
<p>那么，如果不把输入图给卷一卷的话会怎么样呢？打个比方：</p>
<p>假设张三只有一个孩子，那么当他关注孩子时，看到的自然只有这一个孩子，他所了解的有关自己孩子的信息自然都是这一个孩子的。</p>
<p>但如果张三有一百个孩子，他再关注孩子时，看到的肯定不只是一个孩子，他了解到的有关自己孩子的信息就变成了一百个。我们可以把这个类比成深度学习中感受野的增大。</p>
<p>也就是说，当张三对孩子的“感受野”比较小时，即孩子比较少时，他能准确地说出每个孩子的性格特点；但是当张三对孩子的“感受野”，即孩子很多以后，他就只能说出他的孩子们一些大概的性格特点和共同点，很难再详细地去关注每一个孩子了。</p>
<p>综上所述，我们可以理解为：<strong>感受野大的时候，机器更容易标出较大的物体，但是很容易忽略较小的物体，反之亦然。</strong></p>
<h2 id="特征图"><a href="#特征图" class="headerlink" title="特征图"></a>特征图</h2><p>对原始输入图片进行完卷积处理后，我们会得到一个7x7x30的图。</p>
<p>对于7*7的网格，我们会分配每个网格两个比例不同的框，然后经过一系列筛选，最终选出了机器认为最合适的那个框框。</p>
<p>对于30，其实是30=20+1+4+1+4，其中20是VOC数据集的类别，其余的是预测框的调整参数和置信度。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413153441452.png" alt="image-20230413153441452"></p>
<h2 id="损失函数"><a href="#损失函数" class="headerlink" title="损失函数"></a>损失函数</h2><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413153614836.png" alt="image-20230413153614836"></p>
<ul>
<li><p>位置误差：位置误差的计算要从x,y,w,h这四个数值下手。坐标也就是x和y的计算就是如图计算。<br>w和h的计算要注意，这里给它们加上了根号。原因是为了让w和h在较小范围内变化时误差变化能明显一些，如下图，小方框的位置偏移度明显比大方框的大，如果不开根号，体现不出来。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413153708258.png" alt="image-20230413153708258"></p>
</li>
<li><p>置信度误差：这里的含不含object指的是当前格子标出来的框框里有没有包含那二十个种类里的某一类，也就是这一段是不是背景。至于为什么要把它们分开，是因为一张图里肯定是背景占了更大部分，而总误差的计算结果是把各部分误差加起来，如果不分开的话最终的计算会变得效果很差。</p>
<p>同时，为了达到更好的效果，还特地给不含object的置信度误差计算公式加上了权重参数，用于减小它的作用。</p>
<p>在含object的算式里，置信度的真实值被设置为1；在不含object的算式里，置信度的真实值被设置为0。</p>
</li>
<li><p>分类误差：这里就是概率值互减，比如要预测是不是恐龙，就求预测的是猫的概率和是猫的真实的概率的差异值。</p>
</li>
</ul>
<h2 id="优缺点"><a href="#优缺点" class="headerlink" title="优缺点"></a>优缺点</h2><p>优点：不用说自然是快了，人家就是为了实时检测开发出来的。</p>
<p>缺点：</p>
<ul>
<li>每个框框都只能检测一个类别。也就是说，但标出来的框框中出现多个类别，例如人和狗同时出现，YOLO_v1只做得到识别其中一个。</li>
<li>对于小物体的检测效果一般。正如我们前面所说，感受野大了，看到的东西多了，对于小物体小细节自然无法做到面面俱到。</li>
</ul>
<p>而YOLO后续版本的开发，就是基于保持速度这一优势的同时，努力解决以上两个缺点。</p>
<h1 id="yolo-v2"><a href="#yolo-v2" class="headerlink" title="yolo v2"></a>yolo v2</h1><h2 id="网络结构-1"><a href="#网络结构-1" class="headerlink" title="网络结构"></a>网络结构</h2><p>在神经网络中，YOLO_v2舍弃了全连接层，加入了BN(Batch Normalization)层，对网络的每一层输入都做了归一化。这使得收敛更加容易，减少过拟合。</p>
<p>输入图像的分辨率上有所调整。v1输入图像训练使用224x224，测试时是使用448X448,这很有可能导致模型“水土不服”。而v2在训练时又加上了十次448x448的微调。</p>
<p>yolov2的backbone采用的是DarkNet19</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413161931986.png" alt="image-20230413161931986"></p>
<h2 id="特征图-1"><a href="#特征图-1" class="headerlink" title="特征图"></a>特征图</h2><p>先验框提取方式的改变。v2中使用聚类算法来提取先验框，v2里聚类算法的距离并不是一般的距离，而是让距离等于（1-IOU）。</p>
<p>Anchor Box。通过引入Anchor Box,使得能够检测的目标变多，是解决v1中一个小方格只能检测一个目标的一个方法。</p>
<p>框框的偏移。一般来说，把框框弄出来后肯定需要调整框框的位置，因为很难做到一开始就完全准确。v1版本的调整，是直接设置框框偏移的距离，而v2版本的偏移是设置相对于网格的偏移量。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/8300cb2167754723975d3de3f405f166.png" alt="偏移"></p>
<p>v2版本会在每个小方格中通过聚类算法寻找到5个中心店，每个中心点会形成一个小框框，也就是说相比于v1，v2中每个小方格的框框要多了3个。如果直接设置偏移量的话，可能会发生中心店移动到小方格以外的情况。为了防止这样的情况发生，v2采用了相对网格偏移的方法。</p>
<p>如图，该中心点在横纵方向上都位于第二个方格，那么cx和cy的大小就是一个方格的长度，如果位于第三个方格的话，那么cx和cy的大小就是两个方格的长度，以此类推。</p>
<h2 id="特征融合"><a href="#特征融合" class="headerlink" title="特征融合"></a>特征融合</h2><p>前面有提到，当感受野过大时，较小的物体会比较容易被忽略，这也是v2的网络会面临的问题。为了解决这个问题，v2不单单只会选取感受野最大的图，还会把结果得到的图与之前的图进行特征融合。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/cd45b92d7aca449f886ca68b185520b9.png" alt="特阵融合"></p>
<h1 id="yolo-v3"><a href="#yolo-v3" class="headerlink" title="yolo v3"></a>yolo v3</h1><p>yolov3在2018CVPR的《YOLOV3:An  Incremental Improvement》,v3除了网络结构，其余变动不多，主要是将当今一些较好的检测思想融入到了YOLO中，在保持速度优势的前提下，进一步提升了检测精度，尤其是对小物体的检测能力。具体来说，YOLOv3主要改进了网络结构、网络特征及后续计算三个部分。</p>
<h2 id="网络结构-2"><a href="#网络结构-2" class="headerlink" title="网络结构"></a>网络结构</h2><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/ac5ff41080e946a8b858df6783422c65.jpg" alt="v3网络结构"></p>
<p>1、Darknet53具有一个重要特点是使用了残差网络Residual，Darknet53中的残差卷积就是首先进行一次卷积核大小为3X3、步长为2的卷积，该卷积会压缩输入进来的特征层的宽和高，此时我们可以获得一个特征层，我们将该特征层命名为layer。之后我们再对该特征层进行一次1X1的卷积和一次3X3的卷积，并把这个结果加上layer，此时我们便构成了残差结构。通过不断的1X1卷积和3X3卷积以及残差边的叠加，我们便大幅度的加深了网络。残差网络的特点是容易优化，并且能够通过增加相当的深度来提高准确率。其内部的残差块使用了跳跃连接，缓解了在深度神经网络中增加深度带来的梯度消失问题。</p>
<p>2、Darknet53的每一个卷积部分使用了特有的DarknetConv2D结构，每一次卷积的时候进行l2正则化，完成卷积后进行BatchNormalization标准化与LeakyReLU。普通的ReLU是将所有的负值都设为零，Leaky ReLU则是给所有负值赋予一个非零斜率。</p>
<h2 id="多尺度预测"><a href="#多尺度预测" class="headerlink" title="多尺度预测"></a>多尺度预测</h2><p>YOLOv3使用的方法有别于SSD，虽然都利用了多个特征图的信息，但SSD的特征是从浅到深地分别预测，没有深浅的融合，而YOLOv3的基础网络更像是SSD与FPN的结合。</p>
<p>从<strong>特征获取预测结果的过程</strong>可以分为两个部分，分别是：</p>
<ul>
<li>构建<strong>FPN特征金字塔进行加强特征提取</strong>。</li>
<li>利用<strong>Yolo Head对三个有效特征层进行预测</strong>。</li>
</ul>
<p><strong>a、构建FPN特征金字塔进行加强特征提取</strong><br>在特征利用部分，YoloV3提取多特征层进行目标检测，一共提取三个特征层。<br>三个特征层位于主干部分Darknet53的不同位置，分别位于中间层，中下层，底层，三个特征层的shape分别为(52,52,256)、(26,26,512)、(13,13,1024)。</p>
<p>在获得三个有效特征层后，我们利用这三个有效特征层进行FPN层的构建，构建方式为：</p>
<ol>
<li>13x13x1024的特征层进行5次卷积处理，处理完后利用YoloHead获得预测结果，一部分用于进行上采样UmSampling2d后与26x26x512特征层进行结合，结合特征层的shape为(26,26,768)。</li>
<li>结合特征层再次进行5次卷积处理，处理完后利用YoloHead获得预测结果，一部分用于进行上采样UmSampling2d后与52x52x256特征层进行结合，结合特征层的shape为(52,52,384)。</li>
<li>结合特征层再次进行5次卷积处理，处理完后利用YoloHead获得预测结果。</li>
</ol>
<p><strong>特征金字塔可以将不同shape的特征层进行特征融合，有利于提取出更好的特征。</strong></p>
<p><strong>b、利用Yolo Head获得预测结果</strong><br>利用FPN特征金字塔，我们可以获得三个加强特征，这三个加强特征的shape分别为(13,13,512)、(26,26,256)、(52,52,128)，然后我们利用这三个shape的特征层传入Yolo Head获得预测结果。</p>
<p>Yolo Head本质上是一次3x3卷积加上一次1x1卷积，3x3卷积的作用是特征整合，1x1卷积的作用是调整通道数。</p>
<p>对三个特征层分别进行处理，假设我们预测是的VOC数据集，我们的输出层的shape分别为(13,13,75)，(26,26,75)，(52,52,75)，<strong>最后一个维度为75是因为</strong>该图是基于voc数据集的，它的类为20种，YoloV3针对每一个特征层的每一个特征点存在3个先验框，所以预测结果的通道数为<strong>3x25</strong>；<br>如果使用的是coco训练集，类则为80种，最后的维度应该为255 = 3x85，三个特征层的shape为(13,13,255)，(26,26,255)，(52,52,255)</p>
<p>其实际情况就是，输入N张416x416的图片，在经过多层的运算后，会输出三个shape分别为(N,13,13,255)，(N,26,26,255)，(N,52,52,255)的数据，对应每个图分为13x13、26x26、52x52的网格上3个先验框的位置。</p>
<h2 id="损失函数-1"><a href="#损失函数-1" class="headerlink" title="损失函数"></a>损失函数</h2><p>前面的V1和V2都是只有正反例区分，但是在V3中还有了忽略样本。</p>
<p>正例：任取一个ground truth，与4032个框全部计算IOU，IOU最大的预测框，即为正例。并且一个预测框，只能分配给一个ground truth。例如第一个ground truth已经匹配了一个正例检测框，那么下一个ground truth，就在余下的4031个检测框中，寻找IOU最大的检测框作为正例。ground truth的先后顺序可忽略。正例产生置信度loss、检测框loss、类别loss。预测框为对应的ground truth box标签（需要反向编码，使用真实的x、y、w、h计算出  ）；类别标签对应类别为1，其余为0；置信度标签为1。</p>
<p>忽略样例：正例除外，与任意一个ground truth的IOU大于阈值（论文中使用0.5），则为忽略样例。忽略样例不产生任何loss。</p>
<p>负例：正例除外（与ground truth计算后IOU最大的检测框，但是IOU小于阈值，仍为正例），与全部ground truth的IOU都小于阈值（0.5），则为负例。负例只有置信度产生loss，置信度标签为0。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163216675.png" alt="image-20230413163216675"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163235792.png" alt="image-20230413163235792"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163252439.png" alt="image-20230413163252439"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163308026.png" alt="image-20230413163308026"></p>
<h1 id="yolo-v3-ssp"><a href="#yolo-v3-ssp" class="headerlink" title="yolo v3 ssp"></a>yolo v3 ssp</h1><p>YOLO-V3-SPP主要在YOLO-V3的基础上加了很多trick以及引用SSP结构。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163518611.png" alt="image-20230413163518611"></p>
<h2 id="Mosaic图像增强"><a href="#Mosaic图像增强" class="headerlink" title="Mosaic图像增强"></a>Mosaic图像增强</h2><p>Mosaic数据增强，就是将四张图片通过缩放等手段拼接在一起，增加单张图片内目标数。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163615593.png" alt="image-20230413163615593"></p>
<h2 id="SSP模块"><a href="#SSP模块" class="headerlink" title="SSP模块"></a>SSP模块</h2><p>通过对不同感受野的最大池化，最终进行维度拼接，可以获取到不同尺度的特征融合信息，从而提升模型性能。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163646825.png" alt="image-20230413163646825"></p>
<p>根据模型对比图，我们发现v3和v3spp区别就在于第一个Set块中间加入了SPP部分</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230413163836651.png" alt="image-20230413163836651"></p>
<h2 id="损失函数-2"><a href="#损失函数-2" class="headerlink" title="损失函数"></a>损失函数</h2><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171450226.png" alt="image-20230420171450226"></p>
<p><strong>IOU LOSS</strong>：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171514782.png" alt="image-20230420171514782" style="zoom:67%;"></p>
<p>优点：能够更好的反应重合程度、具有尺度不变性</p>
<p>缺点：当不相交时loss为0</p>
<p><strong>GIoU Loss</strong>：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171622692.png" alt="image-20230420171622692"></p>
<p>其中$A^c$是两个框框的最小包容矩形面积。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171716357.png" alt="image-20230420171716357"></p>
<p><strong>DIoU Loss</strong>：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171824012.png" alt="image-20230420171824012"></p>
<p>DIoU损失能够直接最小化两个boxes之间的距离，因此收敛速度更快。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171839611.png" alt="image-20230420171839611"></p>
<p><strong>CIoU Loss</strong>：</p>
<p>一个优秀的回归定位损失应该考虑到3种几何参数：重叠面积 中心点距离 长宽比</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/image-20230420171903828.png" alt="image-20230420171903828"></p>

                
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                            摘要在黑盒对抗攻击的场景中，目标模型的参数未知，攻击者旨在在查询预算下基于查询反馈找到成功的对抗扰动。由于反馈信息有限，现有的基于查询的黑盒攻击方法往往需要多次查询来攻击每个良性示例。为了降低查询成本，我们提出利用跨历史攻击的反馈信息，称为
                        
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